Description Usage Arguments Details Value Author(s) See Also Examples

Functions that provide visualization of palettes to help determine appropriate contexts where thay can be used.

1 2 3 4 5 6 7 8 9 10 11 12 13 | ```
rancurves(colorset, ...)
ranpoints(colorset, N=10, ...)
swatch(colorset, main=deparse(substitute(colorset)))
swatchHue(colorset, main=paste(deparse(substitute(colorset)),
", by Hue", sep=""))
swatchLuminance(colorset, main=paste(deparse(substitute(colorset)),
", by Luminance", sep=""))
ranswatch(colorset, main=deparse(substitute(colorset)))
uvscatter(colorset, main=deparse(substitute(colorset)), ...)
luminance(colorset, main=deparse(substitute(colorset)), ...)
plothc(colorset, main=deparse(substitute(colorset)), ...)
plotpc(colorset, main=deparse(substitute(colorset)), ...)
p3d(colorset, main=deparse(substitute(colorset)), ...)
``` |

`colorset` |
a character vector containing hexadecimal color values. |

`main` |
a character string, the main title for a plot |

`N` |
an integer; the number of points to plot in each color. |

`...` |
additional graphical parameters. |

Different palettes are useful in different contexts. For example, high luminance colors may work well in barplots but provide low contrast when used to color points in scatter plots. The best way to decide if a palette is right for any particular application is probably to create a sample plot using the palette. The functions described here provide sample plots that display colors.

The function `rancurves`

produces a set of sine curves with
different phases and amplitudes, with each curve shown in a different
color. The function `ranpts`

produces a scatter plot showing
`N`

clustered points in each of the palette colors.

There are four functions that use barplots to display the palette. The
simplest one, `swatch`

, simply produces one bar of height one for
each color, in the order that they are listed in the palette. The
next two, `swatchHue`

and `swatchLuminance`

, first sort the palette
(by hue or by luminance, respectively), before producing the
barplot. The goal of these functions is to make sure that similar
colors can be distinguished by placing them close together. The final
function, `ranswatch`

, randomly sorts the colors, to help decide if
similar colors are identifiable when they are relatively far apart.

The `p3d`

function plots the palette colors as spheres in
three-dimensional CIE L*u*v* color space. It has been shown that
perceptual distance is closely related to Euclidean distance in L*u*v*
space. The `uvscatter`

function produces a scatter plot of the
palette colors using their projected u-v coordinates. The
`luminance`

function sorts the colors by luminance and produces a
scatter plot showing the luminance.

The `plothc`

function performs hierarchical clustering on the
colors (using Euclidan distance in CIE L*u*v* color space and Ward's
linkage) and displays the resulting dendrogram. The `plotpc`

function uses the same distance metric to compute and plot principal
components.

In general, these functions are used for their side-effect (producing
plots) rather than for their return values. In most cases, they
invisibly return the color set with which they were invoked. The
barplot-based functions (`swatch`

, `ranswatch`

, `swatchHue`

, and
`swatchLuminance`

), however, return the vector of bar-centers, which
can be used to add other information to the plot. The `plothc`

function returns the dendrogram, and the `plotpc`

function
returns the principal components object.

Kevin R. Coombes <[email protected]>

1 2 3 4 5 6 7 8 9 10 11 |

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